code: def get_mask(self, n, device):
      if self.mask is not None and self.mask.shape[-1] >= n:
          return self.mask[:n, :n]
  
      mask = torch.ones((n, n), device=device, dtype=torch.bool).triu(1)
      self.register_buffer("mask", mask, persistent=False)
      return mask
  
imports: import torch
  import torch.nn.functional
  from einops import rearrange
  from torch import matmul, nn
label_desc: Use image node_id 2 for calling the Attention obj's underlying member method code with example usage: obj.get_mask(n, device) and returns: mask, self